Robust tire forces estimation system

ABSTRACT

A tire state estimation system is provided for estimating normal force, lateral force and longitudinal forces based on CAN-bus accessible sensor inputs; the normal force estimator generating the normal force estimation from a summation of longitudinal load transfer, lateral load transfer and static normal force using as inputs lateral acceleration, longitudinal acceleration and roll angle derived from the input sensor data; the lateral force estimator estimating lateral force using as inputs measured lateral acceleration, longitudinal acceleration and yaw rate; and the longitudinal force estimator estimating the longitudinal force using as inputs wheel angular speed and drive/brake torque derived from the input sensor data.

FIELD OF THE INVENTION

The invention relates generally to tire monitoring systems forcollecting measured tire parameter data during vehicle operation and,more particularly, to a system for estimating tire forces based upontire sensor-based measurements in combination with vehicle-basedsensor-measured data.

BACKGROUND OF THE INVENTION

An accurate and robust estimation of tire normal, lateral andlongitudinal forces is important for certain vehicle safety, control,and operating systems. Achievement of a system for making reliableestimations of tire forces, however, has proven to be problematic. Inparticular, achievement of a robust system and method for estimatingtire forces based upon indirect tire and vehicle sensor measurementsover the lifetime of a tire tread has eluded the industry.

It is accordingly desirable to achieve such a robust system thataccurately and reliably measures tire forces in vehicle-supporting tiresin real time during vehicle operation.

SUMMARY OF THE INVENTION

According to one aspect of the invention, a tire state estimation systemis provided for estimating normal force, lateral force, and longitudinalforce on a tire mounted to a wheel and supporting a vehicle. The vehiclehas a CAN-bus for delivering vehicle sensor-measured input sensor datafrom a multiple CAN-bus accessible vehicle-mounted sensors includingacceleration and angular velocities, steering wheel angle measurement,angular wheel speed of the wheel, roll rate, pitch rate and yaw rate.The system employs a normal force estimator operable to estimate anormal force on the tire from a summation of longitudinal load transfer,lateral load transfer and static normal force using as inputs lateralacceleration, longitudinal acceleration and roll angle derived from theinput sensor data; a lateral force estimator operable to estimate alateral force on the tire from a planar vehicle model using as inputsmeasured lateral acceleration, longitudinal acceleration, and yaw ratederived from the input sensor data; and a longitudinal force estimatoroperable to estimate a longitudinal force on the tire from a wheelrotational dynamics model using as inputs wheel angular speed anddrive/brake torque derived from the input sensor data.

According to a further aspect, the system employs a roll and pitch angleestimator, an acceleration bias compensation estimator, a center ofgravity estimator, a tire rolling radius estimator, a mass estimatoroperable to generate a vehicle mass estimation from the tirelongitudinal force estimation and a road grade angle input, a center ofgravity longitudinal position estimator and a yaw inertia adaptationmodel operable to generate a yaw inertia output from the vehicle massestimation.

In yet another aspect, the input sensor data excludes data from a globalpositioning system and data from a vehicle suspension sensor.

Definitions

“ANN” or “Artificial Neural Network” is an adaptive tool for non-linearstatistical data modeling that changes its structure based on externalor internal information that flows through a network during a learningphase. ANN neural networks are non-linear statistical data modelingtools used to model complex relationships between inputs and outputs orto find patterns in data.

“Aspect ratio” of the tire means the ratio of its section height (SH) toits section width (SW) multiplied by 100 percent for expression as apercentage.

“Asymmetric tread” means a tread that has a tread pattern notsymmetrical about the center plane or equatorial plane EP of the tire.

“Axial” and “axially” means lines or directions that are parallel to theaxis of rotation of the tire.

“CAN bus” is an abbreviation for controller area network.

“Chafer” is a narrow strip of material placed around the outside of atire bead to protect the cord plies from wearing and cutting against therim and distribute the flexing above the rim.

“Circumferential” means lines or directions extending along theperimeter of the surface of the annular tread perpendicular to the axialdirection.

“Equatorial centerplane (CP)” means the plane perpendicular to thetire's axis of rotation and passing through the center of the tread.

“Footprint” means the contact patch or area of contact created by thetire tread with a flat surface as the tire rotates or rolls.

“Groove” means an elongated void area in a tire wall that may extendcircumferentially or laterally about the tire wall. The “groove width”is equal to its average width over its length. A grooves is sized toaccommodate an air tube as described.

“Inboard side” means the side of the tire nearest the vehicle when thetire is mounted on a wheel and the wheel is mounted on the vehicle.

“Kalman filter” is a set of mathematical equations that implement apredictor-corrector type estimator that is optimal in the sense that itminimizes the estimated error covariance when some presumed conditionsare met.

“Lateral” means an axial direction.

“Lateral edges” means a line tangent to the axially outermost treadcontact patch or footprint as measured under normal load and tireinflation, the lines being parallel to the equatorial centerplane.

“Luenberger observer” is a state observer or estimation model. A “stateobserver” is a system that provide an estimate of the internal state ofa given real system, from measurements of the input and output of thereal system. It is typically computer-implemented, and provides thebasis of many practical applications.

“MSE” is an abbreviation for mean square error, the error between and ameasured signal and an estimated signal which the Kalman filterminimizes.

“Net contact area” means the total area of ground contacting treadelements between the lateral edges around the entire circumference ofthe tread divided by the gross area of the entire tread between thelateral edges.

“Non-directional tread” means a tread that has no preferred direction offorward travel and is not required to be positioned on a vehicle in aspecific wheel position or positions to ensure that the tread pattern isaligned with the preferred direction of travel. Conversely, adirectional tread pattern has a preferred direction of travel requiringspecific wheel positioning.

“Outboard side” means the side of the tire farthest away from thevehicle when the tire is mounted on a wheel and the wheel is mounted onthe vehicle.

“Peristaltic” means operating by means of wave-like contractions thatpropel contained matter, such as air, along tubular pathways.

“Sensor” means a device mounted to a vehicle or to a tire for thepurpose of measuring a specific vehicle or tire parameter andcommunicating the parameter measurement either wirelessly or via avehicle CAN-bus for application.

“PSD” is power spectral density (a technical name synonymous with FFT(fast fourier transform).

“Radial” and “radially” means directions radially toward or away fromthe axis of rotation of the tire.

“Rib” means a circumferentially extending strip of rubber on the treadwhich is defined by at least one circumferential groove and either asecond such groove or a lateral edge, the strip being laterallyundivided by full-depth grooves.

“Sipe” means small slots molded into the tread elements of the tire thatsubdivide the tread surface and improve traction, sipes are generallynarrow in width and close in the tires footprint as opposed to groovesthat remain open in the tire's footprint.

“Tread element” or “traction element” means a rib or a block elementdefined by having a shape adjacent grooves.

“Tread Arc Width” means the arc length of the tread as measured betweenthe lateral edges of the tread.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be described by way of example and with reference tothe accompanying drawings in which:

FIG. 1 is a wheel rotational dynamics model and longitudinal forceestimation made therefrom.

FIG. 2 is a planar vehicle model and the lateral force estimation madetherefrom.

FIG. 3A is a vertical force estimation model.

FIG. 3B is a vehicle representation used in making a vertical forceestimation from the model of FIG. 3A.

FIG. 3C is a vertical force estimation method using designatedalgorithms.

FIG. 4 is a table of vehicle inertial parameters used in estimation oflongitudinal, lateral, and vertical forces.

FIG. 5A is a summary flow diagram for the robust estimation of tireforces with vehicle model parameter adaptation.

FIG. 5B is a flow diagram showing parameter estimation blocks outlined.

FIG. 6A is a graph showing rolling radius sensitivity to tire load.

FIG. 6B is a graph showing rolling radius sensitivity to tire inflationpressure.

FIG. 7A is a graph showing rolling radius sensitivity to speed.

FIG. 7B is a graph showing rolling radius sensitivity to tire wearcondition.

FIG. 8 is a schematic on the method of updating tire rolling radiusbased upon vehicle speed.

FIG. 9A is a graph showing vehicle speed estimation based on correlationanalysis of time dependent signals to show algorithm validation.

FIG. 9B is a graph showing cross-correlation coefficient over time inestimating vehicle speed in comparison with actual vehicle speed.

FIG. 10A is a graph showing experimental validation via track testing offorce estimations, comparing estimated with measured Fx for a front lefttire.

FIG. 10B is a graph similar to FIG. 10A but for the front right tire.

FIG. 10C is a graph similar to FIG. 10A but for the rear left tire.

FIG. 10D is a graph similar to FIG. 10A but for the rear left tire.

FIGS. 11A and 11B are graphs showing experimental validation via tracktesting of force estimation F_(y) comparing estimated with measuredF_(y) for front and rear tires respectively.

FIG. 12 is a graph showing experimental validation via track testing oftire load estimation F_(z) for a front left tire and comparing measuredwith estimated load values.

DETAILED DESCRIPTION OF THE INVENTION

Referring initially to FIG. 4, a summary of the subject robust tireforce estimation system and method is shown by the table 10 presented. Atire 12 creates a contact patch 14 on a ground surface as it rotates.Tire forces F_(x) (longitudinal), F_(z) (vertical) and F_(y) (lateral)are created within the tire and identification of such forces are usedto determine vehicle performance parameters. As seen in table 10, thegoal of the subject system and method is to estimate the listed vehicleinertial parameters (column 16) using standard vehicle sensors such asaccelerometers and a gyroscope, i.e. signals available on major vehiclecontroller area networks (CAN-bus). The subject system force estimate ismade without using global positioning system (GPS) or suspension. Theforces F_(x), F_(y), F_(z) are estimated using the model identified foreach in column 18 as will be explained below. While a number ofalternative approaches for estimating such forces have been proposed,they unanimously use fixed vehicle parameters such as inertialparameters to estimate the tire forces. However, depending on how thevehicle is loaded, inertial parameters of the vehicle, including mass,moments of inertia, and spatial components for location of center ofmass, can have different magnitudes. The subject system and method ismore robust in avoiding the use of load-dependent inertial parameters.

With reference to FIG. 1, the longitudinal force estimation approach ofthe subject system is represented. From the wheel rotation dynamicsmodel 20, the equations shown are generated. Model inputs for the model20 are shown in table 22 to include wheel angular speed and drive/braketorque. The model parameter is rolling radius and the model outputyields individual tire longitudinal force (F_(x)).

FIG. 2 shows the scheme for estimation of lateral force (F_(y)). Aplanar vehicle model 24 is used in the estimation, generating thedynamic equations shown. In table 26, the model inputs of lateralacceleration, longitudinal acceleration and yaw rate are used todetermine the model parameters of mass, longitudinal center of gravity(CoG) and yaw moment of inertial. The model outputs front and rear axlelateral force (F_(y)). For the equations shown, F_(xi) is thelongitudinal force of each wheel, F_(yi) is the lateral force of eachwheel (where fl, fr, rl and a represent the front left, front right,rear left, and rear right wheel, respectively, hereinafter inclusive)and F_(yf) and F_(yr) are the lateral forces of the front and rear axle,respectively. δ is the steering angle of the front wheels, m is the massof the vehicle, a_(x) and a_(y) are the longitudinal and lateralaccelerations of the vehicle, respectively, γ is the yaw rate of thevehicle, I_(z) is the moment of inertia of the vehicle, I_(f) and I_(r)are the distances from the center of mass of the vehicle to the frontaxle and rear axle, respectively, and 2t is the wheel base. Inputs,parameters and outputs for the model are as indicated in table 26.

Referring to FIGS. 3A through 3C, the vertical force estimation used inthe subject system and method are described. The vertical tire forcescan be estimated by the summation of longitudinal load transfer, lateralload transfer and static normal force. FIG. 3A represents a vehiclelateral dynamics model showing the vehicle CoG and identifying modelparameters. FIG. 3B shows a vehicle longitudinal dynamics model and CoGfor the vehicle. In FIG. 3C, equations are identified from which tocalculate an estimation of longitudinal load transfer, lateral loadtransfer and static normal force. The table 32 identifies the modelinputs, model parameters and model output of individual tire verticalforce (F_(z)).

A diagram of the robust estimation of tire forces with vehicle modelparameter adaptation is seen in FIG. 5A. Information from CAN-bussensors is shown in broken line arrows while the internal stateestimates are shown in solid line arrow. A 6D IM U 34 providesacceleration and angular velocities from the CAN-bus. Steering input 36and wheel speed 38 are likewise provided by means of the vehicleCAN-bus.

Acceleration measurements, roll rate, pitch rate and yaw rate areprovided from a 6D IM U unit 34 mounted to the vehicle and available byCAN-bus with steering input 36 and wheel speed 38. A kinematics basedroll and pitch angle estimator 40 receives the acceleration, roll rate,pitch rate and yaw rate and provides an estimation of roll and pitchangles to a RLS CoG height estimation model (1 DOF roll model) 48 toyield a height estimation h_(cg). The acceleration data a_(x) and a_(y)are used in an acceleration bias compensation adjustment 46 to yieldcompensated acceleration measurement a_(xc) and a_(yc). The compensatedacceleration measurements a_(xc) and a_(yc) with height estimationh_(cg) are inputs to a tire dynamic load estimator 54 with CoGlongitudinal position estimation a, b from estimator 52 and massestimation m from estimator 50. The tire dynamic load estimator 54outputs a load estimation normal force (F_(z)) 60.

Wheel speed, engine torque and braking torque available from the CAN-busas inputs to a tire longitudinal force estimator (SMC) 42 with tirerolling radius estimation 44 to yield longitudinal force estimationsF_(xfl), F_(xfr), f_(xrl) and F_(xrr) 64. The longitudinal forceestimations are inputs with road grade θ and longitudinal accelerationa_(x) to a longitudinal dynamics mass estimation model 50. An estimationof mass m is generated by the model 50. Mass m is used in a yaw inertiaadaptation model 56 that uses regression equations to approximatemoments of inertia L.

The load estimation F_(z) from the tire dynamic load estimator 54, thecompensated acceleration data a_(xc) and a_(yc), the yaw inertialadaptation I_(z), mass “m” and CoG position estimation a, b are inputsto an axle force estimator configured as a 3 DOF Planar (SMC) model 58.Lateral force (F_(y)) 62 is an estimation output from the axle forceestimator 58.

The model equations used in creating the normal force (F_(z)) 60, thelateral force (F_(y)) 62, and the longitudinal force (F_(x)) 64estimations from the system and method of FIG. 5A are as describedpreviously.

In FIG. 5B, the parameter estimation blocks are outlined in broken lineas indicated. The parameters estimated are tire rolling radius 44, mass50, CoG longitudinal position 52, yaw inertia adaptation 56 and CoGheight 48. The derivation of tire rolling radius 44 is explained below.Vehicle sprung mass and longitudinal CoG position are derived as setforth in issued U.S. Pat. No. 8,886,395 entitled DYNAMIC TIRE SLIP ANGLEESTIMATION SYSTEM AND METHOD issued Nov. 11, 2014, and U.S. PatentPublication No. 2014/0278040 entitled VEHICLE DYNAMIC LOAD ESTIMATIONSYSTEM AND METHOD filed Mar. 12, 2013 and published Sep. 18, 2014. U.S.Pat. No. 8,886,395 and Application Publication No. 2014/0278040 areincorporated herein by reference in their entireties. The yaw inertiaadaptation 56 is estimated using regression equations that approximatemoments of inertia. Such equations are set forth and discussed in thepaper authored by Allen R. Wade, et al. entitled “Estimation ofPassenger Vehicle Inertial Properties and Their Effect on Stability andHandling” No. 2003-01-966. SAE Technical Paper, 2003, which paper beingincorporated herein in its entirety. The CoG height estimation 48 is setforth in co-pending U.S. Patent Publication No. 2014/0114558 entitledVEHICLE WEIGHT AND CENTER OF GRAVITY ESTIMATION SYSTEM AND METHOD, filedOct. 19, 2012, and published Apr. 24, 2014, incorporated herein in itsentirety by reference.

It will be seen from FIGS. 5A and 5B that the subject estimates oflongitudinal force, lateral force and vertical force are “robust” in thesense that the estimates of vehicle inertial parameters use standardvehicle sensors such as accelerometers and a gyroscope, signalsavailable on major vehicle controller area networks. Global positioningsystem (GPS) or suspension displacement sensors are not used. Hence, thesubject system and method for making its force estimates are GPSindependent and suspension displacement measurement independent andconsequently are referred to as “robust”.

The methodology for estimation of rolling radius 44 will be understoodfrom the experimentally derived sensitivity graph 66 of FIG. 6A (load),graph 68 of FIG. 6B (pressure), graph 70 of FIG. 7A (speed), graph 72 ofFIG. 7B (wear). The sensitivity of rolling radius to load is the slopeof the line of FIG. 6A or 0.9 mm/300 pounds. The sensitivity of rollingradius to tire pressure in FIG. 6B is seen as 0.45 mm/4 psi. Thesensitivity of rolling radius to speed is seen in FIG. 7A as 1.8 mm/40kph. The sensitivity to tire wear is seen in FIG. 7B as 0.22677 mm/3 mm.Tire rolling radius is thus shown to be a function of load, pressure,speed and tire wear state with increasing load and decreasing treaddepth acting to decrease rolling radius and increasing pressure andincreasing speed acting to increase rolling radius.

The rolling radius can therefore be updated as seen in FIG. 8 by vehiclespeed estimation based on correlation analysis of time dependent signals74. Wheel speed in the equation shown is obtained from the CAN-bus ofthe vehicle as seen at 76 while rolling radius (static) r is recursivelyestimated under constant speed conditions using a recursive leastsquares algorithm as seen at block 78. Vehicle speed estimation is shownschematically in FIGS. 9A and 9B and is based on correlation analysis oftime dependent signals. The graph 80 graphs spindle acceleration forboth front and rear wheels as a first step. In FIG. 9B,cross-correlation coefficient against lag [sec] is graphed at 82. Thepeak in the graph 82 of FIG. 9B indicates that disturbances in signalsare most similar at these time delay values. For example, from the rawsignal of FIG. 9A the cross-correlation coefficient graph 82 isgenerated.

The algorithm speed [mph]=(wheel base [m]/lag time [sec]) is used inestimating speed. FIG. 9B indicates a lag time of 0.1609 seconds, fromwhich an estimated speed of 39.95 mph is determined through applicationof the algorithm. The actual vehicle speed of 40 mph compares favorablywith estimated, whereby validating use of the algorithm above. It willbe noted that this method is only applicable when the vehicle is drivingwith constant velocity. A varying vehicle velocity would result in asmearing of the peak in FIG. 9B in the cross correlation function sincethe peak shifts with increasing velocity to the left and decreasingvelocity to the right. Once the speed estimation is made, it may be usedto update the rolling radius estimation pursuant to use of the algorithmof FIG. 8.

The force estimation made pursuant to the methodology of FIGS. 5A, 5Bmay be validating via track testing using the following vehicleparameters:

-   -   m=1722; % kg    -   m_(s)=1498; % kg    -   m_(u)=m-ms; % kg    -   a=1.33; % m    -   b=1.33; % m    -   t=1.619; % m    -   h_(cg)=0.545; CG height from ground % m    -   h_(r)=0.13; % roll center height from ground % m    -   h_(a)=0.1; % unsprung mass height from ground % m    -   c_(roll)=1000; % roll damping N-sec/m    -   k_(roll)=1300% roll stiffness Nm/deg.

Measured force hub readings are compared to estimated with the resultsshown in FIGS. 10A through 10D in experimental validation of F_(x). FIG.10A in graph 84 shows F_(x) for the front left tire, graph 86 of FIG.10B for the front right, graph 88 of FIG. 10C for the rear left andgraph 90 of FIG. 10D for the rear right. Measured vs. estimated showsgood correlation.

Validation of F_(y) estimations using the subject system and method areshown in FIG. 11A graph 92 and FIG. 11B graph 94 for the front and rearaxles, respectively. Validation of F_(z) (tire load estimate) is seen ingraph 96 of FIG. 12. Again, good correlation is seen between measuredand estimated force values, indicating validation of the subject systemand method.

From the foregoing, it will be appreciated that the subject systemestimates tire state forces in a robust, accurate and flexible mannerthrough use of CAN-bus accessible sensor data. From the schematics ofFIGS. 5A, 5B, and the pending U.S. patent applications incorporated byreference herein and issued U.S. Pat. No. 8,886,395 likewiseincorporated by reference herein, the subject system estimates normalforce, lateral force and longitudinal force on a tire by accessing avehicle CAN-bus for vehicle sensor-measured information. Vehicles areequipped with a multiple CAN-bus accessible, vehicle mounted sensorsproviding by the CAN-bus input sensor data. Such input sensor dataincludes acceleration and angular velocities, steering wheel anglemeasurement, angular wheel speed of the wheel, roll rate, pitch rate andyaw rate. The estimation system disclosed deploys a normal forceestimator to estimate a normal force on the tire from a summation oflongitudinal load transfer, lateral load transfer and static normalforce using as inputs lateral acceleration, longitudinal acceleration,and roll angle derived from the input sensor data. The system furtherdeploys a lateral force estimator to estimate a lateral force on thetire from a planar vehicle model using as inputs measured lateralacceleration, longitudinal acceleration and yaw rate derived from theinput sensor data. The system further deploys a longitudinal forceestimator operable to estimate a longitudinal force on the tire from awheel rotational dynamics model using as inputs wheel angular speed anddrive/brake torque derived from the input sensor data.

The schematics of FIGS. 5A and 5B show use by the system of a roll andpitch angle estimator to generate a roll angle estimation and a pitchangle estimation from the input sensor data; an acceleration biascompensation estimator to generate bias-compensated acceleration datafrom the roll estimation, the pitch estimation, and the input sensordata; a center of gravity estimator to generate a center of gravityheight estimation from the roll angle estimation, the pitch angleestimation, and the input sensor data; a tire rolling radius estimatorto generate a tire rolling radius estimation from the input sensor data;a mass estimator to generate a vehicle mass estimation from the tirelongitudinal force estimation and a road grade angle input; a center ofgravity longitudinal position estimator to generate a vehiclelongitudinal center of gravity estimation; and a yaw inertia adaptationmodel to generate a yaw inertia output from the vehicle mass estimation.

Finally, it will be noted that the subject system configures the CAN-businput sensor data to exclude data from a global positioning system anddata from a suspension displacement sensor. Avoidance of the use of GPSand suspension displacement sensor data makes the inputs to theidentified estimators more predictable, accurate, and less susceptibleto erroneous sensor readings. As a result, the subject method isconsidered “robust” and capable of estimation of tire forces in realtime on a consistently accurate basis. Such force estimations may thenbe advantageously applied to various vehicle operating systems such assuspension and braking systems for improve vehicle operability andcontrol.

Variations in the present invention are possible in light of thedescription of it provided herein. While certain representativeembodiments and details have been shown for the purpose of illustratingthe subject invention, it will be apparent to those skilled in this artthat various changes and modifications can be made therein withoutdeparting from the scope of the subject invention. It is, therefore, tobe understood that changes can be made in the particular embodimentsdescribed which will be within the full intended scope of the inventionas defined by the following appended claims.

What is claimed is:
 1. A tire state estimation system for estimatingnormal force, lateral force, and longitudinal force on a tire mounted toa wheel and supporting a vehicle, comprising: the vehicle including aCAN-bus; a plurality of sensors mounted to the vehicle and in electroniccommunication with the CAN-bus; the sensors generating input sensordata, the input sensor data comprising: acceleration and angularvelocities, steering wheel angle measurement, angular wheel speed of thewheel, roll rate, pitch rate, and yaw rate; a normal force estimator inelectronic communication with the CAN-bus and receiving the input sensordata from the sensors, the normal force estimator being operable toestimate a normal force on the tire from a summation of longitudinalload transfer, lateral load transfer and static normal force using asinputs lateral acceleration, longitudinal acceleration and roll anglederived from the input sensor data; a lateral force estimator inelectronic communication with the CAN-bus and receiving the input sensordata from the sensors, the lateral force estimator being operable toestimate a lateral force on the tire from a planar vehicle model usingas inputs measured lateral acceleration, longitudinal acceleration andyaw rate derived from the input sensor data; and a longitudinal forceestimator in electronic communication with the CAN-bus and receiving theinput sensor data from the sensors, the longitudinal force estimatorbeing operable to estimate a longitudinal force on the tire from a wheelrotational dynamics model using as inputs wheel angular speed anddrive/brake torque derived from the input sensor data.
 2. The tire stateestimation system of claim 1, further comprising: a roll and pitch angleestimator in electronic communication with the CAN-bus and receiving theinput sensor data from the sensors, the roll and pitch angle estimatorbeing operable to generate a roll angle estimation and a pitch angleestimation from the input sensor data; an acceleration bias compensationestimator in electronic communication with the CAN-bus and receiving theinput sensor data from the sensors, the acceleration bias compensationestimator being operable to generate bias-compensated acceleration datafrom the roll estimation, the pitch estimation and the input sensordata; a center of gravity estimator in electronic communication with theCAN-bus and receiving the input sensor data from the sensors, the centerof gravity estimator being operable to generate a center of gravityheight estimation from the roll angle estimation, the pitch angleestimation and the input sensor data; a tire rolling radius estimator inelectronic communication with the CAN-bus and receiving the input sensordata from the sensors, the tire rolling radius estimator being operableto generate a tire rolling radius estimation from the input sensor data;a mass estimator in electronic communication with the CAN-bus andreceiving the input sensor data from the sensors, the mass estimatorbeing operable to generate a vehicle mass estimation from the tirelongitudinal force estimation and a road grade angle input; a center ofgravity longitudinal position estimator in electronic communication withthe CAN-bus and receiving the input sensor data from the sensors, thecenter of gravity longitudinal position estimator being operable togenerate a vehicle longitudinal center of gravity estimation; and a yawinertia adaptation model in electronic communication with the CAN-busand receiving the input sensor data from the sensors, the yaw inertiaadaptation model being operable to generate a yaw inertia output fromthe vehicle mass estimation.
 3. The tire state estimation system ofclaim 2, wherein the longitudinal force estimator is operable togenerate the tire longitudinal force estimation from the tire rollingradius estimation, an engine torque input, and a braking torque input.4. The tire state estimation system of claim 2, wherein the normal forceestimator is operable to generate the normal force on the tireestimation from the center of gravity height estimation, the center ofgravity longitudinal position estimation and the vehicle massestimation.
 5. The tire state estimation system of claim 2, wherein thelateral force estimator is operable to generate the lateral force on thetire from the input sensor data including a measured lateralacceleration, a measured longitudinal acceleration and the yaw rate. 6.The tire state estimation system of claim 5, further comprising: an axleforce estimator in electronic communication with the CAN-bus, the axleforce estimator being operable to generate a lateral force estimationfrom the vehicle mass estimation, the yaw inertia output, the tiredynamic load estimation, the center of gravity longitudinal positionestimation, the bias-compensated acceleration data, a steering wheelangle input, a yaw rate input and the tire dynamic load estimation. 7.The tire state estimation system of claim 2, wherein the accelerationand angular velocities, the pitch rate, the yaw rate and the roll rateare generated from a six degree inertial measuring unit mounted to thevehicle.
 8. The tire state estimation system of claim 2, wherein theroll and pitch angle estimator is based upon a kinematics model of thevehicle.
 9. The tire state estimation system of claim 2, wherein thecenter of gravity estimator is based upon a one degree of freedom rollmodel employing a recursive least squares algorithm.
 10. The tire stateestimation system of claim 2, the tire longitudinal force estimator isbased upon an application of a wheel dynamics model using as modelinputs the wheel angular speed and a measured drive and brake torque.